The present invention relates generally to data image processing and, more specifically, to generating enhanced digital images utilizing image segmentation processes that involve depth buffer information in association with point selection projection images.
The development of high resolution 3D (three-dimensional) imaging techniques, such as magnetic resonance imaging (MRI), computed tomography (CT), and rotation X-ray CT angiography (XRCTA), have led to situations where high volumes of image data are generated in the study of individual patients. In many cases, such as in 3D magnetic resonance angiography (MRA) and 3D CT angiography (CTA), the amount of detail per two-dimensional slice is small, but the high resolution detail of the full image volume is required in the diagnostic process. In one example, arterial beds are generally sparse, so there is typically a large amount of nonvascular image data present in a 3D angiographic image. As the resolution and volume coverage of MRA examinations have increased recently, having adequate means to effectively and efficiently review the available image information is a significant challenge for radiologists. Currently, there are three primary display techniques used for clinical evaluation of MRA data. The first technique is to display selected original cross-sectional MRA images. The second technique is to display oblique planar reformatted images. The last technique is to display a maximum intensity projection (MIP) image. The following discussion focuses mostly on MRA images; however, the discussion is equally applicable to other 3D angiography techniques such as CTA and XRCTA and to other volumetric visualization techniques in which selectable groups of image points are of interest.
The original cross-sectional images, also referred to as slices, contain the maximum amount of information on a local level. Each image displays the transverse segments and cross sections of vessels that intersect the plane of the image. Sometimes, the vascular detail of interest may be observed in a single image. More often, because of the intricate paths of the vessels, many images must be viewed, and the information from each image must be integrated to formulate an understanding of the structure of interest. This method is inefficient since it requires a single slice to be displayed, which slice contains very little global information about any single intricate vessel in the overall vascular network.
The oblique planar reformatted images provide improved efficiency over the original images by displaying image planes that follow a segment of a vessel of interest through the three-dimensional image data. Each individual image still provides only local information and a complete view of the vascular bed is not obtained. Volume rendering consists of the projection or rendering of an entire 3D image volume onto a single two-dimensional image. The volume is projected along parallel or diverging lines through the three-dimensional volume onto a two-dimensional image. The intensities along the projection line are transformed according to some specified transformation. Such rendering, in a variety of forms, has become very useful in assisting the observer in the rapid and efficient interpretation of the large amounts of image data originally obtained. A simple form of volume rendering, which is also intuitive, is an X-ray-like summation of the image densities or intensities. Initial attempts at MRA found that when the summation type of volume rendering was used, the background signal was too large to the point of masking a majority of vascular details during the summation process. The display capabilities were improved to the point where useful vascular details were observable when the rendering was performed by selecting only the maximum image value encountered along each projection line. This approach is known as the maximum intensity projection (MIP) algorithm and has been used in the development of a variety of MRA techniques. Other forms of volume rendering, such as assigning opacities and translucensies to certain image values or regions of image values, have been applied and found useful. However, the MIP algorithm is dominant because of its simplicity and consistency in generating quality images.
The MIP algorithm is successful to the extent that the signal from vessels is greater than the signal of the surrounding tissues. In regions where vessels do not appear overlapped, the MIP algorithm is sufficient to display vessels that are hyperintense relative to the variations in the overall background. Unfortunately, the MIP algorithm does not provide any information about vessels that are hidden below the intensity of other structures. Because of the sparseness of vessels, there is a significant amount of vessel detail that is not hidden and the MIP performs very well in increasing the amount of vessel detail observed in a single image.
Although there is a loss of information in a MIP image, the MIP algorithm provides a large amount of useful information in a single display. The information density, or information content per image element, is much higher in a MIP image than in the original 3D source data. In other words, although the source image data contains more total information, including some small arteries that would not appear in the MIP image, the density of vessel information in the source image data (i.e. the percentage of image elements associated with vessels) is lower than in the MIP image. As discussed later, many investigators have tried to develop algorithms to overcome the limitations of the MIP algorithm, and, although these have been viewed as improvements, the improvements have not been sufficient for any of these algorithms to replace the MIP algorithm. The advantages of the MIP algorithm typically outweigh the disadvantages found therein. These advantages include-reduced dynamic range, generally consistent image display, and improved signal difference to noise ratio (SDNR or contrast to noise ratio) for vessels that appear in the MIP. The artifacts in the MIP, although very real, are also well understood and can be xe2x80x9cread through.xe2x80x9d
The MIP display contains a large amount of global information about the vascular system. The appearance of a MIP image is quite similar to that of an X-ray angiogram, but there are several differences. The MIP simply selects the image element with the maximum intensity along each projection line, while the X-ray projection is a summation of all densitometric information along each projection line. The MIP image is therefore a flat display having no depth information and no information about the thickness of the vessel through which the projection line passed, while in X-ray angiography the vessel brightness or darkness depends directly on the length of the X-ray projection path through the vessel. Thus, in regions where several vessels are overlapping in the MIP image, it is difficult to resolve specific vessel segments. Further, because the signal intensity is not related to the projection path length through the vessel, an increase in vessel signal is not observed for foreshortened vessels. There are other limitations that exist in the MIP algorithm. Statistical nosie properties associated with the signal of the tissue background cause the signal level of the background in the MIP to increase with the projection volume thickness. Consequently, small vessels with signal levels that are slightly above the local background signal level may be lower than other background image elements along the projection line and, therefore, may not be observed in the MIP images. The limitations of the MIP can make it necessary to also review the original low information density cross-sectional images. When used in combination, the two display formats are a means for detecting and evaluating many types of vascular pathology. The review process, however, is rather time consuming, especially for complex cases.
Various attempts have been made to overcome the deficiencies that exist in the MIP algorithm. The appearance of small vessels in the MIP image can be improved by the use of zero filled interpolation (ZFI). The problem addressed by ZFI is that the arbitrary positions of the vessels relative to the reconstruction grid cause small vessels or vessel borders to be poorly rendered in the image due to the weighting of the voxel sensitivity function. ZFI reconstructs the image on a grid that is finer than the acquisition grid, thereby reducing vessel jaggedness in the MIP image caused by partial volume effects and improving vessel continuity and visibility in the MIP image for subvoxel-sized vessels.
Other attempts to overcome the problems inherent in the MIP algorithm and to improve the two-dimensional rendering of MRA image data range from simple modifications of the MIP algorithm, to attempts at complete segmentation and redisplay of the vascular information contained in the original MRA data. These attempts include utilizing the traveling or sliding slab MIP technique; preprocessing the original MRA image data before application of the MIP algorithm; line-based segmentation, where the vessel voxels are segmented from the background image data, which is also similar to segmentation work performed in other fields; intensity based segmentation, where the vessel voxels are segmented based upon relative intensities and proximity in 3D; and interactive intensity based segmentation, where vessel voxels that cannot be segmented based upon intensity are displayed over a wider gray scale range than those that can be segmented.
MRA display capabilities are also useful in a number of intracranial applications. There are various disease processes for which the diagnosis can benefit from improved image display capabilities. These diagnostic capabilities include predicting the need for thrombolysis in stroke conditions, diagnosing vasculitis and other occlusive diseases, identifying intracranial tumors and arterial venous malformations, and performing preoperative assessments. Further, MRA and other 3D angiographic images provide useful assistance for surgical procedures. The display of catheter angiograms and/or MRA image data have been found to be important aids during aneurysm surgery. The usefulness of the display of 3D angiographic image data during surgery can be enhanced by the display of the angiographic images in conjunction with 3D images of other types of anatomy such as adjacent bone structures or critical organs.
According to the present invention, a method and system are disclosed for enhancing and segmenting a multi-dimensional image based upon the depth buffer (or xe2x80x9cZ-bufferxe2x80x9d) that is generated in conjunction with a maximum intensity projection (or related point selection projection) operation through the multi-dimensional image. This enhancement and segmentation is referred to as the depth-buffer segmentation (DBS) process. The DBS process segments an MA image volume into regions having a likelihood of vessel occupation and regions that are unlikely to contain vessels. The process reduces the total image volume to a much smaller volume so that the amount of territory that a user must cover is greatly reduced. In addition, projection images of the vascular information found within the data become much more refined and visible when the background regions have been removed.
The DBS process merges the global properties of the MIP process with the local properties of continuity to achieve a high specificity in the segmentation of vessel voxels from background voxels while maintaining a high sensitivity. The segmentation provides an accurate and robust view of the vessel structure and filters out most of the non-vessel regions in the image. After the vascular detail is segmented, it can then be converted back to image data for displayxe2x80x94for example, as a densitometric summation of the MRA image data resembling that of an X-ray angiogram. In an optional step, the dynamic range of the display is reduced by hollowing out the vessel structures and displaying the vascular details as X-ray projection through hollow tubes. Further, the image may be modified by adding a degree of shading so that the 3D vessel structure is apparent in a manner not visible in X-ray images. Such image shading was not possible in the MIP process.
In one embodiment, the method generates a reduced dimensionality image data set from a multi-dimensional image by formulating a set of projection paths through image points selected from the multi-dimensional image, selecting an image point along each projection path, analyzing each image point to determine spacial similarities with at least one other point adjacent to the selected image point in a given dimension, and grouping the image point with the adjacent point or spacial similarities between the points is found thereby defining the data set. The method, within the analyzing step, the step of determining similarity of brightness between the image point and the adjacent point. Further, the analyzing step also determines similarity of smoothness between the image point and the adjacent point. In one example, the smoothness is determined by using a least squares fit of adjacent image points. In yet an alternative embodiment, the method further includes selecting another image point along the projection path and performing the analyzing and grouping steps on the newly selected image point. Further still, the method may also convert the grouped and ungrouped image points into a multi-dimensional image and then perform region growing within the converted multi-dimensional image or perform hollowing-out of the multidimensional image for image enhancement. Another image enhancement steps include removing all pixels that are surrounded on each side by an adjacent pixel prior to displaying the image of the merged and unmerged image points.
The system further defines an image processing apparatus that comprises means for defining a set of projection paths through a multi-dimensional image, means for selecting at least one point, along each projection path, based upon a specified criterion, means for formulating an array of projection values corresponding to the positions of selected points along their respective projection path, and means for grouping a selected number of projection values based upon their proximity to other projection values. The apparatus essentially is a programmable computer system that loads a particular software program capable of implementing the steps of the method claims within a computer architecture environment for manipulating the data and processing it for imaging, whether the imaging is in a printed image or a displayed image, such as on a computer monitor. Further still, the method may be implemented in a computer program product for sale or distribution, whether that product is in a portable medium or in a fixed medium or remote medium that is communicated via a communications source, such as the Internet or other communication means known to those skilled in the art.